This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Cognitive Information Processing Technology: Augmented Cognition on the Future Battlefield The goal of the Augmented Cognition on the Future Battlefield proposal is to enable a quantum leap forward in operator-workstation effectiveness by creating a cognitive closed-feedback loop between the operator and an adaptive workstation. The system architecture being developed will be applicable to any battlefield system. Boeing will use the Unmanned Combat Air Vehicle (UCAV) as a demonstrator test bed for an augmented cognition prototype workstation. The Augmented Cognition UCAV workstation will enable UCAV operators to control more aircraft by augmenting the cognition of individual operators. The proposed augmented cognition architecture will leverage the UCAV workstation and the UCAV Decision Aiding System (UDAS). The UCAV/UDAS architecture consists of 4 main components, the UCAV workstation, the UCAV simulation (both vehicle and scenario simulations), UDAS, and the UDAS cognitive workload assessor. [unreadable][unreadable]The UCAV workstation interface is highly modular, and supports easy and efficient rapid prototyping. It takes input from UDAS and reconfigures the workstation display. The UCAV simulation is a full-featured simulation, capable of inserting many high workload mission events such as pop-up targets, and contingency events. [unreadable][unreadable] The UDAS cognitive workload assessor currently is under manual control by the operator. The augmented cognition cognitive workload assessor will automate this function. The Augmented Cognition program is now in Phase 3. Phase 1 investigated physiological and performance metrics of cognitive workload and those results were incorporated into later Phases. The principle deliverable for Phase 2 was a demonstration of an adaptive, closed-loop operator-workstation system in which the workstation responds to increasing levels of operator cognitive workload by adaptively increasing the automation level of selected tasks and reconfiguring operator displays, thereby lowering cognitive workload. During Phase 3 the workstation will be stress-tested with increasingly complex and realistic scenarios to push the envelope of the operator-workstation team.